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U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

CENTERS FOR DISEASE CONTROL

NATIONAL INSTITUTE FOR OCCUPATIONAL

SAFETY AND HEALTH 

+ + + + +

ADVISORY BOARD ON RADIATION AND

WORKER HEALTH

+ + + + +

SEC ISSUES WORKGROUP

+ + + + +

FRIDAY

JANUARY 24, 2014

+ + + + +

The Work Group convened via 

teleconference at 11:00 a.m., Eastern 

Daylight Time, James M. Melius, Chairman, 

presiding

PRESENT:

JAMES M. MELIUS, Chairman

JOSIE BEACH, Member

GENEVIEVE S. ROESSLER, Member

PAUL L. ZIEMER, Member
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ALSO PRESENT:

TED KATZ, Designated Federal Official

BOB BARTON, SC&A

HARRY CHMELYNSKI, SC&A

ARJUN MAKHIJANI, SC&A

JOYCE LIPSZTEIN, SC&A

JOHN MAURO, SC&A

JAMES NETON, DCAS

MICHAEL RAFKY, HHS

DANIEL STANCESCU, DCAS

JOHN STIVER, SC&A

TIM TAULBEE, DCAS
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T-A-B-L-E O-F C-O-N-T-E-N-T-S

PAGE

Introduction

Chairman Melius ........................... 3

NIOSH Update on Report 0053/sufficient

accuracy analyses

Dr. Jim Neton ............................. 4

SC&A Review Update

Mr. John Stiver .......................... 33

Dr. Harry Chmelynski ..................... 35

Dr. Joyce Lipsztein ...................... 46

Comments and Questions ................... 56

Plans for Work Group & January ABRWH 

Meeting Session .......................... 59

Adjourn
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1 P-R-O-C-E-E-D-I-N-G-S

2 (11:00 a.m.)

3 CHAIRMAN MELIUS: Okay. Thanks, 

4 Ted. Welcome, everybody. I appreciate you 

5 taking the time. I believe this will be a 

6 relatively brief meeting. It's mostly to 

7 get prepared for the Board Meeting next week 

8 and sort of decide where we are on sort of 

9 dealing with the SEC review group that's 

10 been looking at the whole issue, along with 

11 working with NIOSH and others on ORAU and 

12 SC&A on this issue of sufficient accuracy. 

13 And just wanted to get an update prior to 

14 the meeting and then decide what, if 

15 anything, about this topic we want to 

16 discuss at the meeting next Tuesday.

17 So I think our first order of 

18 business is sort of get an update from where 

19 NIOSH is. And, Jim?

20 DR. NETON: Okay. Thank you, Dr. 

21 Melius. I have a couple brief documents 

22 that I can share with you as to our progress 
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1 in two areas that we were agreed to look 

2 into at the last Working Group Meeting.

3 And that has to do with what I'll 

4 call the 100 millirem experiment where we're 

5 going to add 100 millirem to some NOCTS 

6 cases and see how that affected PC outcome. 

7 And then a little bit on where I am at with 

8 the -- I committed that we would start to 

9 draft an implementation guide for coworker 

10 models. And I've made some progress on 

11 that, but honestly I have more questions 

12 than answers at this point.

13 Regarding the first issue, I've 

14 just got a brief presentation here about the 

15 practical significant dose evaluation. And 

16 just this slide -- can everybody see my 

17 slide, by the way?

18 MEMBER ROESSLER: You know, this 

19 is amazing. This is Gen. I got this 

20 invitation this week to get on this live 

21 stuff on the computer and I've been fussing 

22 with it for a whole day. Ted just sent the 
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1 information, I got on and I see it. This is 

2 great.

3 DR. NETON: Excellent. That's 

4 Gen. Everybody else, too?

5 MEMBER BEACH: Yeah, I do.

6 MEMBER ZIEMER: Yes, I see it, 

7 but it's not centered. Can I do something 

8 about this or can you close the left side of 

9 your screen?

10 MEMBER BEACH: No, you can center 

11 it, Paul. I did.

12 MEMBER ROESSLER: Well, how do 

13 you do it?

14 MEMBER BEACH: Down at the bottom 

15 of your screen.

16 MEMBER ROESSLER: Yeah, you 

17 should be able to click on slideshow and do 

18 it, but that doesn't work.

19 MEMBER BEACH: There's a bar at 

20 the bottom.

21 DR. NETON: Well, I did slideshow 

22 but it's too big, so I kind of left it in 
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1 this mode.

2 MEMBER ROESSLER: Okay, I've got 

3 it centered now.

4 DR. NETON: Just so it can all 

5 fit on the screen. I only have -- this is 

6 my only slide. So we don't have to labor 

7 too much.

8 MEMBER ROESSLER: You have a 

9 bunch of neophytes here.

10 DR. NETON: Yes.

11 MEMBER ZIEMER: Yeah, it's too 

12 big for my screen for some reason.

13 DR. NETON: This is the only one 

14 and all I want to do is just summarize what 

15 we said we were going to do, and then get 

16 into another document that gives us some 

17 preliminary results.

18 MEMBER ZIEMER: Okay.

19 DR. NETON: So just bear with me 

20 on these four bullets here. Just to refresh 

21 your memory, we had proposed to evaluate the 

22 significance -- to attempt to start to 
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1 evaluate the significance of what dose 

2 really makes a practical difference in a 

3 dose reconstruction.

4 We agreed to look at a bunch of 

5 NOCTS claims, and the idea was to identify 

6 NOCTS claims with a single cancer that had a 

7 Probability of Causation between 45 and 50 

8 percent. And those, by definition, are best 

9 estimates, because over 45 percent we're 

10 required to do a best estimate. And we also 

11 felt, if you recall, that anything below 45 

12 percent would be unlikely to be changed by 

13 addition of 100 millirem.

14 And we also -- in the protocol 

15 that we established, we're going to insert a 

16 zero millirem exposure line for each case, 

17 and then do 30 IREP runs of 10,000 

18 iterations for each NOCTS case and calculate 

19 the average PC of all those cases.

20 Between 45 and 50, this is 

21 standard protocol. We're required to do 30 

22 runs of 10,000 because it minimizes the 
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1 uncertainty in the estimate. And then we 

2 were going to change that zero millirem 

3 exposure to 100 millirem, and this is going 

4 to be external dose only, and do the same 

5 thing, do the 30 IREP runs and calculate the 

6 average PC.

7 Well, we selected the cases and 

8 it turns out that, at the end of the day, we 

9 ended up with 175 cases out of about 38,000 

10 claims that had been dose reconstructed that 

11 met our selection criteria.

12 So we went about doing exactly 

13 what I just outlined here, and it took a lot 

14 of computer horsepower. We moved a lot of 

15 electrons around doing this analysis, and 

16 these are preliminary results because 

17 honestly we just got them a few days ago.

18 And so all I'm going to be able 

19 to present here is sort of a brief sketch of 

20 what we ended up seeing. And, of course, 

21 there's a lot analysis to do here on these 

22 data sets, but I wanted to give you a flavor 
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1 for where we are at this point.

2 First table here just sort of 

3 summarizes what -- it does summarize the 

4 frequency distribution of the cancers that 

5 came out of that 174 or 175 case set.

6 What surprised me is about half 

7 the cases -- almost half the cases were 

8 either lung cancers or non-melanoma basal 

9 cell carcinomas, which really surprised me. 

10 I thought it would be more of an even 

11 distribution, or more likely I thought the 

12 leukemias would be in that category, but 

13 they weren't. There was only three 

14 leukemias, excluding chronic lymphocytic 

15 leukemia, that met the criteria.

16 So, anyway, this is a 

17 distribution of the cancers that we saw. If 

18 anybody has any questions, please chime in, 

19 because again this is very preliminary and 

20 I'm kind of looking at this only for the 

21 second time myself.

22 MEMBER ROESSLER: This is kind of 
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1 -- I remember talking about this at the 

2 meeting, but all of a sudden now having to 

3 think about it and things are flashing 

4 around on my screen, which you're probably 

5 doing.

6 DR. NETON: Yes.

7 MEMBER ROESSLER: What does that 

8 mean, that only 175 out of 38,000?

9 DR. NETON: There were only 175 

10 cases of all the cases that we did dose 

11 reconstruction that had a Probability of 

12 Causation between 45 and 50 percent, or less 

13 than --

14 MEMBER ROESSLER: Oh, okay. I 

15 get it. Okay, I see what you're saying. So 

16 those are the ones then that you will test -

17 -

18 DR. NETON: Exactly.

19 MEMBER ROESSLER: Really what 

20 you're asking is what does 100 millirem do 

21 to the PC?

22 DR. NETON: Exactly.
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1 MEMBER ROESSLER: Okay, I got it.

2 DR. NETON: And this is just for 

3 general interest, you know, which cancers 

4 comprise the 174, 175 cases. And you can 

5 see that about half were between -- 54 out 

6 of those were for lung and 30 were for non7 melanoma BCC.

8 The rest were fairly evenly 

9 distributed. You have, I guess, all male 

10 genitalia and colon cancer represented, next 

11 two highest number of cases. That's sort of 

12 telling us --

13 DR. MAURO: Jim, this is John. 

14 I'm sorry to interrupt, I also have a 

15 question of the nature that Gen just asked.

16 DR. NETON: Yes.

17 DR. MAURO: So you've got this 

18 group that falls into the category of 45 to 

19 50 percent. And the process you went 

20 through, you lost me a little bit on when 

21 you described the zeros and 100 a little. 

22 Conceptually, what I'm seeing is you've got 
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1 these cases and you're adding 100 millirem 

2 to the totality of the external dose, or is 

3 that 100 millirem per year?

4 DR. NETON: No, to the totality. 

5 It's 100 millirem increase in the total 

6 dose.

7 DR. MAURO: Okay. So whatever 

8 the dose was reconstructed using your 

9 standard protocols, including the non10 detects, including the coworker models, and 

11 everything else that went into these 

12 realistic dose reconstructions for all these 

13 cases, you just went ahead and said, okay, 

14 I'm going to add another 100 millirem at 

15 some point in time.

16 DR. NETON: Right.

17 DR. MAURO: Because we are 

18 covering, I guess, multiple years. I guess 

19 you just pick some time, a given year, and 

20 say I'm just going to add in to that year?

21 DR. NETON: Actually, we thought 

22 about this some, and it was in the protocol, 
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1 I didn't go over it, but we decided to pick, 

2 since it was external exposure, we added it 

3 to the first year of employment, that it 

4 would maximize the latency.

5 DR. MAURO: Got you.

6 DR. NETON: We didn't want to put 

7 it too close in time.

8 DR. MAURO: Okay, so you picked 

9 that year. I'm with you. Okay.

10 DR. NETON: We did a slightly 

11 different adjustment for leukemia because 

12 leukemias have a shorter latency.

13 DR. MAURO: Yeah, two years on 

14 that one. Yeah.

15 DR. NETON: And I forget where we 

16 put it, I think we put it at five years out, 

17 the exposure, because that was the maximum 

18 credit that would be given.

19 Now, it might be a little 

20 confusing why we added -- since we already 

21 had the runs, why did we add a zero line?

22 Well, what happens is, you know, 
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1 we reset the zero and put the line in and 

2 reran it, and then when we added the 100 we 

3 reran the same cases with the same random 

4 number seeds so that we could truly look at 

5 the difference between adding 100 millirem, 

6 and sort of isolate the variability that is 

7 due to just the uncertainty in the Monte 

8 Carlo calculation.

9 So, what I'm going to be 

10 presenting, not to be confusing, is I’ve got 

11 three comparisons. I'll have the original 

12 PC value, I'll have the recalculated value 

13 with zero added, and the recalculated value 

14 with 100 millirem added. And you'll see 

15 there are differences.

16 DR. MAURO: Got you.

17 DR. NETON: And the main 

18 difference that you're going to see is that, 

19 if you compare the original run with 100 

20 millirem added, there's more variability 

21 there because they're run on two different 

22 random sets of number seeds and that shows 
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1 the inherent variability of the IREP 

2 calculation itself.

3 DR. MAURO: Good.

4 DR. NETON: We're still looking 

5 into this, so, again, a lot of this is going 

6 to have to go through the gristmill before 

7 we --

8 DR. MAURO: I understand what you 

9 said. Very good, thank you.

10 DR. NETON: All right. And in 

11 fact this next slide shows exactly that.

12 MEMBER ROESSLER: My picture went 

13 away, did I hit something?

14 MEMBER ZIEMER: Mine went away 

15 also. My Live Meeting says nothing is 

16 currently shared.

17 DR. NETON: Well, I've got 

18 something on my screen here, which is 

19 interesting.

20 MEMBER BEACH: Yeah, I've got 

21 nothing, too.

22 DR. NETON: Okay, well, maybe it 
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1 timed out or something. Let me try it 

2 again. I have Paul Ziemer's desktop showing 

3 being shared.

4 MEMBER ROESSLER: Uh oh.

5 MEMBER ZIEMER: Well, maybe I 

6 took you over, but I didn't know I was 

7 sharing anything.

8 DR. NETON: No, you took me over 

9 I think. Let me –

10 MEMBER ZIEMER: How do I undo 

11 that?

12 DR. NETON: I'm going to do it 

13 myself here. Okay, now I'm going to go back 

14 to share and share my desktop.

15 MR. KATZ: All right, that 

16 worked.

17 DR. NETON: Is that back?

18 MEMBER ROESSLER: Yes. That's 

19 back.

20 MEMBER ZIEMER: Okay. I'm seeing 

21 a chart, is that what you're showing?

22 DR. NETON: Yeah, now I'm looking 
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1 at the second table here which is titled 

2 "variable."

3 MEMBER ROESSLER: Okay, very 

4 good. I've got it.

5 DR. NETON: It has a minimum, 

6 medium, and maximum.

7 MEMBER ROESSLER: Mm-hmm.

8 DR. NETON: Okay. This 

9 particular graph, table, shows the direct 

10 comparison. The average PC of the original 

11 174 cases you see a minimum, median, mean, 

12 and maximum.

13 So the mean value of the original 

14 cases, of all the cases added up, the PC was 

15 47.37 percent. When we added the zero dose 

16 the mean value of all the cases when we 

17 reran them with a different random number 

18 seed, was also 47.37, which was good. We 

19 would hope that would be the case.

20 When we added 100 millirem dose 

21 to all 174 cases, the median value of all 

22 the cases rose to 47.43. In other words, a 
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1 0.06 percent increase. So not much, which 

2 was kind of interesting.

3 And if you look at the minimum 

4 and maximum values, of course the original 

5 value had a minimum of 44.9 and a maximum of 

6 49.87. In the cases where we added either 

7 zero or 0.1, none of the cases exceeded 50 

8 percent, which is interesting.

9 So, you know, you have a lot of 

10 cases here that were very close to 50 

11 percent. And, again, we reran all 174 and 

12 not one of them moved over the 50 

13 percentile, or 50 percent of the 99th 

14 percentile. So that was kind of interesting 

15 itself.

16 And so the difference of 0.06 is 

17 pretty small. I expected more, actually. 

18 So we tried to -- you know, Daniel Stancescu 

19 did these comparisons, so I'll give him the 

20 credit here, but, you know, we had a few 

21 days to look at this so we tried to do a few 

22 little breakouts here.
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1 And here we, the next table that 

2 you'll see that has "cancer type" in the 

3 title, shows a comparison of what the 

4 results look like with leukemia cancers 

5 versus solid cancers. And you really don't 

6 see a huge difference. I thought there 

7 might be because leukemias tend to be more 

8 radiosensitive and it might move more with 

9 100 millirem added, but not necessarily. 

10 There was an uptick. If you look 

11 at the average value right here, the average 

12 PC to add 100 millirem dose, you got 47.67. 

13 The average for the solid was a little bit 

14 lower. So there was a little bit higher 

15 increase there, but nothing really that 

16 stuck out in my mind as super significant. 

17 Moving on to the next table, this 

18 is just what I really kind of just said on 

19 the original slide. The mean value changed 

20 0.06 percent for all the cases. Now, you 

21 will see that the spread of differences is 

22 much greater in the add 100 millirem to the 
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1 original versus 100 millirem added to the 

2 zero dose added.

3 You'll see the minimum value was 

4 minus-0.43 and the maximum change was 0.67, 

5 so quite a spread versus zero when we 

6 compare the ones that were run with the same 

7 random number seed to a maximum of 0.34.

8 And what that really reflects is 

9 the inherent variability in the random 

10 number seed generation of the Monte Carlo 

11 calculation. Because the second line 

12 comparison here removes that degree of 

13 uncertainty because we ran them with the 

14 same random number seed.

15 So, moving on, I have another 

16 comparison here of leukemias and nothing 

17 really -- again, there's a slightly higher 

18 difference in the mean values, but nothing 

19 of substance that I think is of note at this 

20 point.

21 Again, further comparisons, 

22 cancer type, not much there. 
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1 Just on a last note, we just 

2 looked at the frequency of changes. That 

3 is, how many went up and how many went down 

4 and we did the comparisons. And if you 

5 compare the average change from the values 

6 when we -- the original to the 100 millirem 

7 dose, compared those two values, the 

8 frequency was 64, went down -- is that 

9 right? Four had no change and 106 went up.

10 That represents, I think, the

11 uncertainty of the Monte Carlo calculation 

12 itself. And that's something we might want 

13 to look into when we're talking about 

14 significant dose, is maybe what degree of 

15 dose is required to show a statistical 

16 significant difference in the result above 

17 and beyond the Monte Carlo uncertainty.

18 And this last slide I have just 

19 shows that when you compare the two that 

20 were run with the same random number seed, 

21 173 went up and 2 had no change. None went 

22 down.
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1 So, I think that's all I want to 

2 say about that. That's all we can really 

3 get right now. But to me the big item is 

4 that none of the cases went over 50 percent 

5 by adding 100 millirem.

6 MEMBER ROESSLER: Will we get a 

7 copy of this data when we get to the 

8 meeting?

9 DR. NETON: When you get to the 

10 meeting?

11 MEMBER ROESSLER: Yeah, or --

12 DR. NETON: Well, I don't know. 

13 I mean, that's -- we're going to decide that 

14 I guess during this call, what we want to do 

15 with this. These are very preliminary. 

16 Again, you know, we just got these done. I 

17 don't know how much time I'm going to have 

18 to clean them up before the meeting.

19 MEMBER ROESSLER: Okay. Well, I 

20 think at some point it would be --

21 DR. NETON: Oh, yeah, sure.

22 CHAIRMAN MELIUS: At some point 
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1 it should be a report.

2 DR. NETON: Yes, exactly. This 

3 was just to give you a heads up as a 

4 completed analysis and here's where we are 

5 right now.

6 CHAIRMAN MELIUS: Yes.

7 DR. NETON: But, yeah, we need to 

8 have a complete analysis and report of this 

9 all.

10 CHAIRMAN MELIUS: Yes. I think 

11 the question may be is -- before we, you 

12 know -- before you write your report or 

13 before we meet, are there other analyses 

14 that we want done?

15 You know, do we want to look at 

16 whether adding in a larger amount --

17 remember we're trying to sort of figure out 

18 what -- how -- I don't know what the right 

19 word would be, but how much variability or 

20 how much, you know, sensitivity is there to 

21 error in some of the comparisons we're 

22 making on coworker analyses and so forth.
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1 And it doesn't appear that 100 

2 millirem, you know, makes that much 

3 difference.

4 DR. NETON: Yeah, and I think 

5 this may have, actually, more importance 

6 down the line in looking at the residual 

7 contamination reconstructions.

8 CHAIRMAN MELIUS: Yeah.

9 DR. NETON: But I'm still not --

10 it's still out whether it really makes a big 

11 difference in the overall dose 

12 reconstruction.

13 CHAIRMAN MELIUS: Right, yeah. 

14 And I don't want to try to push you, you 

15 know, into conclusions, you know, without 

16 giving you a chance to review the data and 

17 sit down and talk to it. But I would say 

18 that we, you know, do that -- and maybe the 

19 first step is to get at least, you know, 

20 give you a little bit more time to review 

21 this and pull it together and then, you 

22 know, either keep it as a presentation and 
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1 do it at another Work Group meeting or, you 

2 know, do it as a report and --

3 DR. NETON: Yeah. We're going to 

4 look at this and I think I already sort of 

5 hinted that we might try to look at the 

6 comparison of the uncertainty of the Monte 

7 Carlo calculation itself versus the addition 

8 of the dose and --

9 CHAIRMAN MELIUS: Yeah.

10 MEMBER ZIEMER: Yeah, Jim, this 

11 is Ziemer. I think that issue is probably 

12 important to pin down in any event, the 

13 uncertainty being the Monte Carlo itself.

14 DR. NETON: Right. And, you 

15 know, I'm trying to tease out here are there 

16 big differences in different cancer models 

17 and stuff. And we're not really seeing 

18 that. You know, I thought maybe for certain 

19 cancers it would, you know, be totally 

20 different.

21 Because each cancer has, of 

22 course, its own radiosensitivity to dose and 
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1 certain latency adjustments and such, but 

2 from this first analysis, at least with 

3 external dose, it doesn't seem -- it seems 

4 sort of spread around pretty evenly.

5 CHAIRMAN MELIUS: Yeah, but you 

6 also have different exposure patterns for 

7 people and so there's probably a fair amount 

8 of noise in these calculations within a 

9 given type of cancer.

10 DR. NETON: Yeah, and I think 

11 it's -- to be honest with you, it's probably 

12 somewhat fortuitous that none of them went 

13 over 50 percent. I think that, you know, I 

14 can't guarantee that if we didn't do 200 

15 comparisons, one or two wouldn't come over.

16 CHAIRMAN MELIUS: Yeah. No, and 

17 I think that's why we got to be a little 

18 careful jumping to --

19 DR. NETON: Right. I'm not 

20 jumping to any --

21 CHAIRMAN MELIUS: -- conclusions 

22 and, you know, sort of what is -- does this 
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1 help us to –- or where do we think 

2 sufficient accuracy is?

3 DR. NETON: Yeah. You know, it 

4 doesn't say much to me right now except, in 

5 my mind, 100 millirem doesn't mean a heck of 

6 a lot.

7 CHAIRMAN MELIUS: Yeah, yeah.

8 DR. NETON: That's about all I 

9 can say.

10 DR. MAKHIJANI: Dr. Melius, this 

11 is Arjun Makhijani.

12 CHAIRMAN MELIUS: Yes?

13 DR. MAKHIJANI: It seems to me 

14 that maybe instead of, you know, inserting 

15 some other number into the same calculation 

16 it might be useful to deliberate a little 

17 bit on what are the uncertainties in 

18 coworker doses, for instance, and whether 

19 they are different for internal exposure and 

20 external exposure. And then to do a 

21 sensitivity analysis based on that.

22 Of course, you know, it's 
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1 difficult to know what the uncertainties are 

2 in many cases, but I think that may be a 

3 more fruitful approach because then you are 

4 actually dealing with, you know, where the 

5 margins of your analysis lie and how many 

6 people might be pushed over if you use a 

7 different percentile and so on.

8 So, you know, there's a 

9 difference between the 84 percentile and the 

10 95 percentile, for instance, and that will 

11 vary from one coworker model to another and 

12 one set of data to another. And maybe it 

13 might be useful to get a glance at what 

14 those numbers are, especially for internal 

15 dose, because external we don't have as many 

16 difficulties in terms of estimation.

17 CHAIRMAN MELIUS: Yeah, though I 

18 think we -- I understand what you're saying 

19 and I think it can be helpful. I'm not sure 

20 I would want to make that step before, you 

21 know, understanding these data a little bit 

22 better. Because I think we still end up in 
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1 a problem, if we go directly to coworker of, 

2 you know, how much difference, you know, is 

3 too much or, you know, what's an appropriate 

4 difference. What kind of difference are we 

5 looking for or can we tolerate on these?

6 DR. MAKHIJANI: Yes, right.

7 MEMBER ZIEMER: Yes, this is 

8 Ziemer. I think though if we went through a 

9 group situation versus the individual.

10 CHAIRMAN MELIUS: Yeah.

11 DR. NETON: This is Jim. I think 

12 -- I agree that we maybe flush this out a 

13 little more and then when we get a handle on 

14 how much difference we're willing to 

15 tolerate, if I can use that word.

16 CHAIRMAN MELIUS: Yes.

17 DR. NETON: Then I think we can 

18 go and look at a couple internal coworker 

19 models as a test case and take it all the 

20 way through, because up till now all we've 

21 been saying is we're comparing the 50th or 

22 the 84th percentiles and saying are they 
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1 different statistically, but you really need 

2 to carry it through the entire intake 

3 calculation, as I pointed out several times.

4 And given that there's going to 

5 be ups and downs on a year-by-year basis, 

6 you fit both sets and determine how do those 

7 come out and compare. That, to me, is the 

8 ultimate test.

9 Now, we would prefer not to do 

10 that for every single coworker model, but we 

11 might be able to do some sort of proof of 

12 principle on a test case or two.

13 CHAIRMAN MELIUS: Yeah. If you 

14 remember our plan out of the last Work Group 

15 meeting was to, you know, try to determine, 

16 you know, how much of a difference we can 

17 tolerate, or whatever you want to -- however 

18 we want to refer to that.

19 And, secondly, then see how that 

20 would -- apply that to external dose models, 

21 simply because they were less complicated 

22 than the internal -- and then go to the 
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1 internal.

2 DR. NETON: Right.

3 CHAIRMAN MELIUS: And I'm a 

4 little hesitant to change that pathway at 

5 this point in time. 

6 Paul, or Gen, or Josie, any 

7 comments or questions?

8 MEMBER ROESSLER: This is pretty 

9 fascinating.

10 CHAIRMAN MELIUS: Yeah.

11 MEMBER ROESSLER: Good work.

12 MEMBER BEACH: I don't have any.

13 DR. MAURO: Jim, what was the 

14 highest case again? The 49 point what?

15 DR. NETON: The highest result?

16 DR. MAURO: Yeah, of all the 

17 cases you looked at, there was one that had 

18 the highest PoC.

19 DR. NETON: Right there, I think 

20 it's 49.87.

21 DR. MAURO: So you're at 49.87, 

22 you then take 100 millirem and you add it 
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1 into the year that you think would have the 

2 greatest effect given latency for that 

3 particular cancer?

4 DR. NETON: Right.

5 DR. MAURO: And you still didn't 

6 move over 50 percent?

7 DR. NETON: Correct.

8 DR. MAURO: Okay, thank you.

9 DR. NETON: And you can see the 

10 average difference is 0.06, so that kind of 

11 falls in that that must of had a somewhat 

12 average increase, because there's others 

13 with higher increase.

14 DR. MAURO: Yes.

15 DR. NETON: I have a suspicion 

16 that the higher the dose the less it makes -

17 - the less difference it makes because it's 

18 not a linear --

19 DR. MAURO: Sure.

20 DR. NETON: -- seen as a linear, 

21 so, you know, maybe the ones with the lower 

22 doses had the most increase. That's the 
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1 kind of stuff we need to be looking at.

2 DR. MAURO: Yeah.

3 DR. NETON: But, yeah, you're 

4 right, John, that's exactly what happened.

5 CHAIRMAN MELIUS: Any other 

6 discussion on this? What I'd like to do, 

7 just get a quick update from SC&A on where 

8 they are, and then come back to decide what 

9 are our next steps and what do we do, if 

10 anything, at the Board meeting next week.

11 DR. NETON: Okay.

12 CHAIRMAN MELIUS: So, John, or --

13 I don't know who's running the show at SC&A. 

14 Sounds like no one.

15 MR. STIVER: This is John Stiver, 

16 I was just getting back on line here.

17 CHAIRMAN MELIUS: Okay, I'm 

18 sorry.

19 MR. STIVER: Yeah, we had a --

20 we're very close to producing our paper on 

21 the kind of consolidation of all of our 

22 positions on OPOS. And it looks like it's 
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1 probably -- before we get cleared and 

2 everything else and have it your hands, it 

3 would be probably another couple weeks.

4 So, I tried to ask Harry to put 

5 together some fundamental kind of 10,000-

6 foot view slides of kind of highlighting our 

7 position on some of these various issues. 

8 Mainly, as a courtesy to NIOSH, to let them 

9 know where we stand, what’s coming and to 

10 inform you all before the meeting next week. 

11 Harry, do you have access to Live 

12 Meeting or would you like me to run through, 

13 just flip the slides for you?

14 DR. CHMELYNSKI: That would be 

15 better if you could do that.

16 MR. STIVER: Okay, let me try to 

17 take over here. Okay, can everybody see 

18 that?

19 MEMBER ROESSLER: Yes, I can see 

20 it.

21 MR. STIVER: Okay.

22 MEMBER ZIEMER: We also have 
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1 these slides, you distributed them.

2 MR. STIVER: Right. Yeah, we 

3 could do that. I thought it might be a 

4 little easier for some of us who are on Live 

5 Meeting to do it this way.

6 MEMBER ZIEMER: Right.

7 MR. STIVER: Either way is fine.

8 MR. STIVER: As long as you have 

9 them you can follow along. So, anyway, 

10 Harry, we're going to Slide 2 here.

11 DR. CHMELYNSKI: All right. 

12 We've been preparing a review on what is 

13 known as the OPOS methodology, and up till 

14 now it's usually taken to mean “one person, 

15 one sample.” But that's a little confusing 

16 because each person has lots of samples and 

17 what we're really talking about is one 

18 person, one statistic derived from those 

19 samples.

20 And, in the simple case, the 

21 statistic we're talking about is just the 

22 average if there's no non-detects. Now, 
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1 OPOS was introduced by NIOSH to address two 

2 main problems that they introduced called 

3 data dominance, where a large number of 

4 samples from a few workers may skew the 

5 distributions. And there's also a problem 

6 with correlation. If there are a lot of 

7 samples taken one after each other, they 

8 would be correlated.

9 So we examined this problem and 

10 how extensive they were at the two sites 

11 where OPOS has been applied, which is 

12 Savannah River and Fernald. I'm moving on 

13 now to the next page.

14 And when there are non-detects, 

15 OPOS is to be calculated using what was 

16 called the maximum possible mean. And this 

17 algorithm that I put here, “Step 1-2-3,” is 

18 taken out of one of the documents that is 

19 used by the analyst to construct the OPOS 

20 values for the sites where the methodology 

21 is being applied now.

22 And the Step 1 says that we're 
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1 going to use the MDA for all data that's 

2 reported less than the MDA. 

3 Step 2 says if all of the samples 

4 for a worker are below the MDA, censored, in 

5 other words, then we have to treat the 

6 answer, the mean, for OPOS as a censored 

7 value.

8 And Step 3, if any of the data 

9 are uncensored then we do the same 

10 calculation, but we treat the mean as a 

11 measured value. 

12 This is probably the most 

13 convenient way to define what OPOS is. When 

14 we looked into how this procedure was 

15 implemented, though, we found some problems, 

16 particularly in Step 1. What we found was, 

17 a lot of cases, they don't explicitly have 

18 the entry as less than some number.

19 They may have a zero there or 

20 they may have a negative number, or they may 

21 actually have a number which, if you look 

22 down the column, they all say 0.1 and the 
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1 rest of them are all less than 0.1, but this 

2 one nobody put the less than next to.

3 So there's lots of ways that data 

4 can be censored in the database, although 

5 sometimes not explicitly censored. And what 

6 we found is that unless there was actually a 

7 notation that said less than 0.3, or 

8 something like that, the number was actually 

9 taken at face value and used in the 

10 calculation for the maximum possible mean.

11 And this happened both at SRS and 

12 at Fernald, on occasions, so we're concerned 

13 that this can lead to some very strange 

14 answers, including negative answers, which, 

15 according to the algorithm, probably should 

16 be computed as non-detects.

17 But sometimes these numbers have 

18 remained in the calculations all the way 

19 through to determining what the coworker 

20 models should be. So that was one of the 

21 problems with implementation of the OPOS 

22 algorithm.
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1 The second application we were 

2 looking at is how OPOS is used to compare 

3 groups of workers. And our main concern 

4 here still applies.

5 We had this as a finding in our 

6 old report, which is when you're comparing 

7 two groups of workers and these workers were 

8 monitored using a different monitoring 

9 program, trying to use a hypothesis test to 

10 compare the two sets of data seems to me not 

11 to make much sense.

12 It's really a case of apples and 

13 oranges in a lot of cases here, especially, 

14 in particular, the comparison that we 

15 concentrate on is comparing onsite workers 

16 with contract workers. And a lot of times 

17 the contract workers weren't monitored the 

18 same way as the onsite workers.

19 So, this problem remains that 

20 we've addressed previously. A new issue, 

21 though, that has come up in response to 

22 NIOSH's response to our review, is that 
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1 there is a justification that they present 

2 for why you should use OPOS. 

3 And now we've changed the name, 

4 really. OPOS is now -- we're going to refer 

5 to it as the mean excretion rate, because 

6 that's what we're trying to estimate when we 

7 take the average of the results for the 

8 period. We're trying to find a mean value. 

9 And NIOSH came up with this 

10 argument that says, well, if you do the 

11 right regression problem and you use the 

12 right weights, you can show that the mean 

13 excretion rate should be proportional to the 

14 intake.

15 Of course, we're trying to find 

16 the intake from these mean values, or from 

17 all the values, however the best way would 

18 be, but the answer that they came up with 

19 was that we should be able to use just the 

20 mean because it is proportional to the 

21 intake.

22 We reviewed the source of this 
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1 calculation and we found that there are 

2 several problems with it and Joyce will 

3 address these later when we get to them.

4 But, for now, let me just say 

5 that both OPOS and the weighted least 

6 squares approach ignores the timing of the 

7 data during the year. And this sometimes is 

8 important and sometimes not. Weighted least 

9 squares also ignores the timing of the 

10 bioassays. But when we use the word “mean 

11 excretion rate,” I think what we're talking 

12 about it the time-weighted average year 

13 excretion rate over the year for the worker, 

14 and that we would think of OPOS as a 

15 statistic trying to estimate that mean.

16 On the next page, then, this is 

17 Page 8, there's an example of when OPOS will 

18 work well. And here's a curve that's 

19 presumably due to some exposure early in the 

20 year and it purports to be the concentration 

21 in the urine of the worker on each day of 

22 the year as you go across the curve.
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1 Now, if we were to monitor this 

2 worker, say, eight times during the year at 

3 nice, equally spaced intervals and then take 

4 the average, the average we get is actually 

5 equal to the mean value of this under this 

6 curve.

7 And, in fact, it's probably one 

8 of the best known ways of doing an integral, 

9 which is to do the Riemann sum and say, ah, 

10 that's what you can get when you do the 

11 integral.

12 I've normalized the X axis so 

13 that it's all one year. It could be two 

14 years in some cases, but as long as you use 

15 one year then the area under the curve is 

16 equal to the mean.

17 And in this case you see the 

18 actual calculation of the true mean, which 

19 is -- this is a cubic function and I was 

20 able to do the integral. It comes out very 

21 close to what the Riemann sum, or the OPOS 

22 calculates.
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1 I'm going to go to the next 

2 slide, though. We see that the picture is a 

3 lot more complicated than thinking about 

4 equally spaced bioassay collections in time.

5 And what this graph shows is how 

6 many days we found between successive 

7 bioassays for plutonium for any given worker 

8 and the frequency count, basically, of how 

9 many of them had 30 days between them, 90 

10 days, et cetera.

11 And you can see pretty clearly 

12 that while there's a tendency to have 

13 testing done every 90 days in that first 

14 spike, or every 180 days, that's half, two a 

15 year. The next one is four a year, and even 

16 out there at 720 you can see where sometimes 

17 it's every two years. But the point of this 

18 slide is that, in general, we don't know 

19 that these workers were being sampled on any 

20 regular basis.

21 This is particularly true for the 

22 construction-type workers who may be in and 
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1 out of the site a lot and may only be tested

2 for a particular reason, if something turns 

3 up.

4 Given, then, that we don't 

5 believe that there were these nice, 

6 regularly spaced sampling for most workers, 

7 we can then think about, well, what is OPOS 

8 telling us if it's not telling us the 

9 integral under the curve?

10 Well, there's another way to 

11 think about it, which is if they are random 

12 sampling times then really what we've done 

13 is we've sampled at eight points along a 

14 curve and those points are just like taking 

15 a Monte Carlo integral to determine what the 

16 area under this curve is.

17 And that kind of calculation, 

18 usually you use a lot more than a handful of 

19 bioassays, such as we're doing here. Here, 

20 I think, you know, eight to ten is about the 

21 most you would reasonably see for any 

22 worker.
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1 But, still, you can think of it 

2 as a Monte Carlo approximation to the 

3 integral with just a small number of 

4 iterations, maybe eight or even less.

5 And if you do that, then, you can 

6 put some statistical statements on what the 

7 precision of your estimate of OPOS is, 

8 thinking of it as a Monte Carlo estimate of 

9 the integral.

10 And, of course, as we already 

11 know, what you're going to end up with is 

12 the Student t-distribution, tells you what 

13 the confidence bounds are for that estimate 

14 of the mean. And, in particular, it's a 

15 Student t-distribution within minus one 

16 degrees of freedom, which we always have to 

17 keep in mind here because when we're staring 

18 to take averages of three or four samples 

19 that gets us into problems.

20 The next page has some formulas 

21 for how you do the calculations for the 

22 upper bound and the lower bound, so I'm not 
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1 going to get into those. But, basically, if 

2 you just look at the picture that I drew 

3 here with the samples, eight samples, and I 

4 did calculate according to these formulas 

5 what the confidence bounds were. 

6 If we go back to page, what was 

7 that, Page 10. And you see the confidence, 

8 the 95 percent confidence bounds almost span 

9 the whole range of the data here. Well, not 

10 all the way up to the top. 

11 But we have eight samples here. 

12 Now, if you only had four, those confidence 

13 bounds would go beyond the range of the 

14 data. So it just makes me wonder why we put 

15 a lot of confidence in this number that we 

16 call OPOS, especially, as we're going to see 

17 soon, almost 95 percent of the time we're 

18 doing it with four or less samples.

19 At any rate, that was some of the 

20 concerns we've had going into this and I 

21 think maybe Joyce can start with the rest of 

22 these slides and give an overview of what 
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1 her concerns were.

2 DR. LIPSZTEIN: Okay. John, 

3 please continue with the slides, I can't do 

4 it from here. We are on Slide 13 and I 

5 think some of this in this slide Harry 

6 already was talking about.

7 So, OPOS was designed to address 

8 the presence of data dominance, which is a 

9 large fraction of samples being submitted by 

10 a small fraction of individuals, and 

11 correlate the date where multiple samples 

12 submitted by individuals can be correlated, 

13 which greatly complicates the use of 

14 statistical tests.

15 Then we go to Slide 14. And we 

16 wanted to know how relevant is the problem 

17 of data dominance. And we wanted to know if 

18 a large number of incident-related samples 

19 from a few workers would skew the 

20 distribution use for coworker modeling. And 

21 we wanted to know how frequently do we find 

22 data dominance in the DOE facilities. So 
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1 that's why we looked at SRS and at Fernald.

2 The next slide, Slide 15, we can 

3 see that -- and Harry already told this --

4 that in over 95 percent of the cases where 

5 OPOS would be applied at SRS, the workers 

6 have no more than four to twenty bioassays 

7 in the period. We did this for all the 

8 radionuclides that we examined, and there's 

9 very few cases where you would have workers 

10 with more than -- we saw a lot of samples 

11 with more than four bioassays.

12 And then we looked at data 

13 dominance at Fernald. So at Fernald we have 

14 one coworker model that was done in 2012 

15 using the coworker method. And we have the 

16 Version 1, which was done in 2010 and was 

17 done with the old methodology.

18 So we could have both of them to 

19 compare, and they are relatively new: 2010, 

20 2012. What we found out is that on the 

21 Revision 1 samples, code 50, which are 

22 samples that were taken on special jobs, 
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1 implementations, they were not considered in 

2 the Revision 1 2010, but they were 

3 considered in the OPOS methodology.

4 And the accident-related samples, 

5 which were codes 40 and 49, were analyzed in 

6 both versions of it. And then we compared 

7 the 50 percent and the 95th percentiles 

8 intake rates derived in Revision 1 and 

9 Revision 2, and we wanted to know how the 

10 addition of samples code 50 would influence 

11 or not these intake rates.

12 And what we found was that there 

13 was no relation. It's not sometimes and 

14 some years, the OTIB 2012 had a higher 

15 intake rate than the one in 2010, but many 

16 times the 2010 had higher intake rates than 

17 2012.

18 And this was not related at all 

19 to the number of samples code 50, and also 

20 it was not related to the code 40 and 49, 

21 years that had more samples than codes 40 

22 and 49. You couldn't establish a 
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1 relationship between those samples.

2 So then we aimed to see what was 

3 the problem of correlations. The fact that 

4 some workers have more samples than other 

5 workers in a given time period is not itself 

6 a basis to establish correlation.

7 We looked at both coworker 

8 models, Revisions 2012 and 2010, and they 

9 both cite the main -- the same problem of 

10 data dependence. And they explicitly, for 

11 example, in the OTIB, the coworker models 

12 from 2012, it's explicitly exemplified that 

13 they take some -- in order to derive the 

14 intakes for 1994-2006 periods.

15 Early intake rates significantly 

16 biased later intake rates for all solubility 

17 types of uranium compounds. So the problem 

18 of data correlation doesn't end with the use 

19 of the OPOS. You still have correlated data 

20 whether there is accidents or there is 

21 routine exposure, it doesn't matter, you 

22 always have data correlation when you have 
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1 internal exposure.

2 Then we went to the problem of 

3 using weighted least squares to justify the 

4 OPOS. The problem is that if you go from 

5 the beginning, in order to justify that it's 

6 been -- that the mean excretion rate would 

7 be proportional to the mean intake rate, you 

8 have to go to least square using weighted 

9 least square.

10 The problem is that the weighted 

11 least square is only justified applying when 

12 there is one intake. And we have this 

13 explicitly said in MCFB 164 2003 13. We 

14 also have that explicitly said in IMBA 

15 application also.

16 And it all starts with the 

17 equation that you have to calculate the 

18 intake, and so in certain special 

19 circumstances you can say that the mean 

20 excretion rate would be very special 

21 circumstance, as you saw in Harry's slides.

22 You can say that the mean 
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1 excretion rate would be proportional to the 

2 mean intake, but that's excretion rates that 

3 are related to that intake. You cannot take 

4 the whole year intakes and take the means 

5 because then -- okay, Slide 19 is what IMBA 

6 says.

7 But then you can see you cannot 

8 take the mean excretion rate to be 

9 proportional to intake when you mix times in 

10 the year, times that there were no intakes 

11 with times that there were intakes.

12 What happens if you have a worker 

13 or a facility where the monitoring was very 

14 heavy so you have frequent monitoring for 

15 the workers before the incident or the 

16 special job? Then you have a smaller OPOS. 

17 If you don't have any monitoring before the 

18 incident or the special sample, then the 

19 OPOS which would be much higher.

20 So what happens is that the OPOS, 

21 in reality, if it's taken on a year basis, 

22 it would be proportional to the frequency of 
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1 monitoring. This you can see on Slide 20.

2 The complication of OPOS for year 

3 average urine activities collected from 

4 periods of no intakes lumped together with 

5 activities from periods with intakes.

6 The consequences are strong 

7 dependence on the frequency of the 

8 monitoring, in addition to the number of 

9 significant exposures. We did an example 

10 that you will see on our paper, we took some 

11 people from Fernald that were exposed in the 

12 same incident. There was an incident in one 

13 of the years that we took as an example, and 

14 we compared, there were three workers, one 

15 worker was only monitored during the 

16 incident, but just one time.

17 Then there was another worker 

18 that was monitored during the incident but 

19 he had several monitoring during this 

20 incident. And then we had the worker that 

21 was monitored many times in the year before 

22 the incident.
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1 And so the same worker worked in 

2 the same incident and we could find that 

3 those workers had similar exposure in this 

4 incident. The OPOS of the person that was 

5 heavily monitored before the -- routinely 

6 monitored before the incident, had the 

7 smaller OPOS.

8 So what I mean with this is that 

9 when you average the OPOS over the year 

10 there is a dependence on the frequency of 

11 monitoring.

12 And, for the same reason, when you compare 

13 two groups of workers, if one group of 

14 workers is only monitored when there are 

15 some kind of incidents or special jobs and 

16 is not monitored before, and then you have a 

17 group of workers that's been monitored both 

18 routinely and when the special job is done 

19 or the incident occurs, then you cannot 

20 compare the two. Because in one of them you 

21 were just comparing the incident or the 

22 special job, and on the other worker you 
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1 were taking into account also the routine 

2 monitoring from when he was not exposed. 

3 So, that's it.

4 CHAIRMAN MELIUS: Okay. Anybody 

5 have questions?

6 MEMBER ZIEMER: Well, this is

7 Ziemer. I --

8 CHAIRMAN MELIUS: It is difficult 

9 to -- I mean, I'm actually finding it very 

10 hard to ask questions. It's very hard to 

11 understand this kind of report from a slide 

12 presentation.

13 DR. MAKHIJANI: Dr. Melius?

14 CHAIRMAN MELIUS: Yes?

15 DR. MAKHIJANI: It might be 

16 helpful maybe if I can give you a bottom 

17 line of where our team wound up in regard to 

18 OPOS.

19 MEMBER ROESSLER: That would be 

20 helpful.

21 DR. MAKHIJANI: So, this is still 

22 in the final wordsmithing stages, but I 
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1 thought it might be helpful if I read you 

2 the words that we have in the final draft.

3 The use of OPOS on an annual or 

4 other fixed period basis, the way NIOSH has 

5 now constructed it, as a general matter does 

6 not appear to be scientifically justified.

7 The use of pooled, individual 

8 bioassay data is recommended despite its 

9 known drawbacks. When there's clear 

10 evidence of data dominance the samples 

11 related to a particular incident may be 

12 averaged to provide a single composite data 

13 point to be inserted into the distribution 

14 of the pooled data.

15 So, the bottom line from Harry 

16 and Joyce have been saying is that there are 

17 some times when you would want to combine 

18 samples, but you don't combine them on a 

19 fixed period or an annual period or any 

20 other period when you have incident-related 

21 samples that are clearly auto-correlated, 

22 then you will combine the samples related to 
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1 that incident into one data point because 

2 it's really relating one intake.

3 And then you put that into the 

4 distribution of pooled data. And so you 

5 have a mixed distribution that consists 

6 primarily of individual bioassay samples 

7 that would have some data points that are 

8 OPOS-like data points, although not as 

9 defined by NIOSH.

10 They'd be one person -- one 

11 incident, one statistic, you might say, 

12 points inserted into a distribution of 

13 bioassay samples. So, that's where we wound 

14 up.

15 CHAIRMAN MELIUS: Okay. Thanks 

16 for the summary, Arjun. Any questions or 

17 comments, Board Members?

18 MEMBER ZIEMER: This is Ziemer 

19 again. I assume we're going to get the 

20 detailed report, as will NIOSH, and then we 

21 will have a chance to study it.

22 CHAIRMAN MELIUS: Yes, that's --
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1 definitely.

2 MEMBER BEACH: This is a lot to 

3 take in. This is Josie.

4 CHAIRMAN MELIUS: Yeah, yeah. 

5 No, I agree.

6 MEMBER ROESSLER: And I'll echo 

7 that. I guess my question is does -- I 

8 couldn't understand it all as they went 

9 through it. Probably the first part was 

10 easier to understand, but if NIOSH 

11 understands it and can respond then I think 

12 we can evaluate it.

13 CHAIRMAN MELIUS: Well, I think 

14 we need a report to be able to --

15 MEMBER ROESSLER: Exactly.

16 MR. STIVER: This is John Stiver. 

17 That report should be in your hands within a 

18 couple of weeks.

19 CHAIRMAN MELIUS: Okay.

20 MR. STIVER: And I agree, it's a 

21 lot to try to assimilate, and the report 

22 goes into -- well, it's more detailed. It 
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1 will be easier to understand once you're 

2 able to go through the entire thing.

3 DR. MAKHIJANI: Well, John, 

4 there's going to be a DOE review, so, you 

5 know, it's going to take -- it may be a 

6 little more than a couple of weeks.

7 CHAIRMAN MELIUS: Okay.

8 MR. STIVER: Two weeks is 

9 probably, maybe optimistic.

10 CHAIRMAN MELIUS: Okay.

11 MR. STIVER: We are kind of 

12 captive to how quickly DOE can get to it.

13 CHAIRMAN MELIUS: Okay. Then you 

14 have to give us time to read it. We'll 

15 figure out a schedule on that. Jim Neton, 

16 do you have anything you want to add?

17 DR. NETON: No. We discussed 

18 this late yesterday like everyone else. So 

19 I haven't had time to really think about it 

20 too much.

21 CHAIRMAN MELIUS: Okay. I want 

22 to go back to -- well, I guess, first of 
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1 all, on this report, this set of slides, I 

2 would have severe qualms about using this at 

3 the Board meeting, because it -- I would 

4 rather put that off until the Work Group has 

5 had a chance to review the report.

6 I don't think it's fair or 

7 appropriate and I think it's going to sort 

8 of confuse issues until we've had a time to 

9 look at it and respond.

10 I don't know if any of the other 

11 Work Group Members feel differently, but --

12 MEMBER ZIEMER: This is Ziemer. 

13 I agree with that completely, and I think 

14 the only thing you need to report to the 

15 Board is that SC&A is completing a review of 

16 the OPOS methodology and we expect a report. 

17 That we had preliminary discussion at this 

18 meeting, but we expect a report in a few 

19 weeks and it'll be analyzed at that point.

20 CHAIRMAN MELIUS: Yeah, okay. 

21 And, Jim Neton, what do you feel comfortable 

22 presenting, if anything, at the Board 
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1 meeting? I mean, should we just give an 

2 update that, you know, you've received, you 

3 know, you're progressing on your report and 

4 SC&A is, you know, progressing on their 

5 deliverable and we're going to, you know, be 

6 getting those sometime in the relatively 

7 near future and then we'll have a Work Group 

8 meeting and then be able to report back?

9 DR. NETON: Yeah, I would be most 

10 comfortable with that.

11 CHAIRMAN MELIUS: Yeah. As 

12 interesting as it is, and it's going to --

13 DR. NETON: It almost raises more 

14 questions than it answers.

15 CHAIRMAN MELIUS: Well, yeah. 

16 That's what I'm concerned, and without 

17 having it in a report with, you know, sort 

18 of explanation and so forth I think it's 

19 hard. And in a Board setting, though, I 

20 think that a lot of the other Board Members 

21 would be interested.

22 Paul, Josie, Gen, does that --
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1 MEMBER BEACH: This is Josie. I 

2 completely agree with that approach.

3 MEMBER ROESSLER: I do, too.

4 MEMBER ZIEMER: Yeah, this is 

5 Ziemer. I do, too.

6 CHAIRMAN MELIUS: Okay. I mean, 

7 in some ways it's tempting to move forward, 

8 but at the same time I think it's hard to 

9 that until NIOSH has had a chance to analyze 

10 and we have a chance to review and discuss 

11 it and so forth and try to bring these 

12 reports together to the extent that we can.

13 So, maybe -- I can't remember how 

14 long we set aside on the agenda. It'll give 

15 us a little bit more Board work time, but 

16 that may be fine.

17 So, any other business? Ted, 

18 anything we need to --

19 MR. KATZ: No, this all sounds 

20 good. And we only have a half an hour set 

21 aside for this anyway, so we can easily cede 

22 that back to Board work time.
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1 CHAIRMAN MELIUS: Okay. Good, 

2 yeah. Okay, anyway, in that case, I thank 

3 everybody for their work and the 

4 presentations of the data and we will look 

5 forward to seeing everybody, at least a 

6 number of you, next week in Kansas City.

7 MEMBER ROESSLER: That depends on 

8 whether the blizzard hits Minnesota again on 

9 Monday.

10 CHAIRMAN MELIUS: Is there 

11 another one coming?

12 MEMBER ROESSLER: Yes. I'm 

13 getting kind of tired of this.

14 CHAIRMAN MELIUS: I confess I 

15 looked at the Kansas City weather the other 

16 day. It looked like it was going to be 

17 cold. I didn't see snow in the forecast.

18 MEMBER ROESSLER: Well, I thought 

19 it looked wonderful.

20 CHAIRMAN MELIUS: Yeah, we've 

21 been, you know, ten below or 20 below the 

22 last few days, so some sympathy. But the 
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1 snow, our snow ended up in New York City and 

2 south entirely. The latest snow storm, 

3 anyway. But I cringe every time I hear 

4 about what's happening up your way, Gen.

5 MEMBER ROESSLER: Yeah, but at 

6 least we don't have as much snow, but it 

7 blows and that's then the problem and it 

8 reduces visibility to nothing and it's hard 

9 to drive then.

10 CHAIRMAN MELIUS: Yeah. No, it's 

11 hard, and I know, Paul, Indiana's been hit.

12 MEMBER ZIEMER: Yeah. We're cold 

13 and below zero, but we're surviving.

14 CHAIRMAN MELIUS: Okay, good, 

15 everybody. And even down in Atlanta I think 

16 it's been cold, Ted.

17 MR. KATZ: I don't think we get 

18 any sympathy though.

19 CHAIRMAN MELIUS: No, you don't. 

20 I had a very irate phone call once when I 

21 was working for NIOSH from the State Health 

22 Officer in North Dakota who couldn't 
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1 understand why nobody was answering their 

2 phones at CDC in Atlanta.

3 And he had looked at the weather 

4 and, you know, all they had had was an inch 

5 of snow, and he couldn't believe that they 

6 were closed down for two days in row. And 

7 had no sympathy. He was trying to track 

8 down some result from something.

9 MEMBER ROESSLER: Well, you know, 

10 it has to do with the amount of traffic that 

11 tries to move, too. In North Dakota there's 

12 not that much.

13 CHAIRMAN MELIUS: Yeah. It's 

14 also, I don't know if Atlanta's any better, 

15 but my experience down there used to be that 

16 they had no snow, you know, equipment at all 

17 and no salt to melt the ice and so forth. 

18 On top of bad traffic.

19 MEMBER ZIEMER: Yeah, we'll see 

20 how Kansas City does.

21 CHAIRMAN MELIUS: Yeah, 

22 hopefully. So, anyway, we'll look forward 
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1 to seeing everybody in Kansas City next 

2 week. Thank you all for your time.

3 (Whereupon, the meeting was 

4 concluded at 12:06 p.m.)

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